SoroushVahidi/scidocs huggingface dataset:
Enterprise Content Management - A Literature Review
(2012)

Managing information and content on an enterprise-wide scale is challenging. Enterprise content management (ECM) can be considered as an integrated approach to information management. While this concept received much attention from practitioners, ECM research is still an emerging field of IS research. Most authors that deal with ECM claim that there is little scholarly literature available. After approximately one decade of ECM research, this paper provides an in-depth review of the body of academic research: the ECM domain, its evolution, and main topics are characterized. An established ECM research framework is adopted, refined, and explained with its associated elements and working definitions. On this basis, 68 articles are reviewed, classified, and concepts are derived. Prior research is synthesized and findings are integrated in a conceptcentric way. Further, implications for research and practice, including future trends, are drawn.
A New Mura Defect Inspection Way for TFT-LCD Using Level Set Method
(2009)

Mura is a typical vision defect of LCD panel, appearing as local lightness variation with low contrast and blurry contour. This letter presents a new machine vision inspection way for Mura defect based on the level set method. First, a set of real Gabor filters are applied to eliminating the global textured backgrounds. Then, the level set method is employed for image segmentation with a new region-based active contours model, which is an improvement of the Chan-Vese's model so that it more suitable to the segmentation of Mura. Using some results from the level set based segmentation, the defects are quantified based on the SEMU method. Experiments show that the proposed method has better performance for Mura detection and quantification.
Text Analytics in Social Media
(2012)

The rapid growth of online social media in the form of collaborativelycreated content presents new opportunities and challenges to both producers and consumers of information. With the large amount of data produced by various social media services, text analytics provides an effective way to meet usres’ diverse information needs. In this chapter, we first introduce the background of traditional text analytics and the distinct aspects of textual data in social media. We next discuss the research progress of applying text analytics in social media from different perspectives, and show how to improve existing approaches to text representation in social media, using real-world examples.
Project Management Process Maturity „ PM ... 2 Model
(2002)

This paper presents the project management process maturity (PM) 2 model that determines and positions an organizatio relative project management level with other organizations. The comprehensive model follows a systematic approach to es organization’s current project management level. Each maturity level consists of major project management characteristics, fa processes. The model evolves from functionally driven organizational practices to project driven organization that incorporates co project learning. The (PM) 2 model provides an orderly, disciplined process to achieve higher levels of project management mat DOI: 10.1061/ ~ASCE!0742-597X~2002!18:3~150! CE Database keywords: Project management; Models; Organizations. ve lan ani uiteron’s r th at ctic
Users of the world , unite ! The challenges and opportunities of Social Media
(2009)

As of January 2009, the online social networking application Facebook registered more than 175 million active users. To put that number in perspective, this is only slightly less than the population of Brazil (190 million) and over twice the population of Germany (80 million)! At the same time, every minute, 10 hours of content were uploaded to the video sharing platform YouTube. And, the image hosting site Flickr provided access to over 3 billion photographs, making the world-famous Louvre Museum’s collection of 300,000 objects seem tiny in comparison. According to Forrester Research, 75% of Internet surfers used ‘‘Social Media’’ in the second quarter of 2008 by joining social networks, reading blogs, or contributing reviews to shopping sites; this represents a significant rise from 56% in 2007. The growth is not limited to teenagers, either; members of Generation X, now 35—44 years old, increasingly populate the ranks of joiners, spectators, and critics. It is therefore reasonable to say that Social Media represent a revolutionary new trend that should be of interest to companies operating in online space–—or any space, for that matter. Yet, not overly many firms seem to act comfortably in a world where consumers can speak so freely Business Horizons (2010) 53, 59—68
Social Media Metrics — A Framework and Guidelines for Managing Social Media
(2013)

Social media are becoming ubiquitous and need to be managed like all other forms of media that organizations employ to meet their goals. However, social media are fundamentally different from any traditional or other online media because of their social network structure and egalitarian nature. These differences require a distinct measurement approach as a prerequisite for proper analysis and subsequent management. To develop the right social media metrics and subsequently construct appropriate dashboards, we provide a tool kit consisting of three novel components. First, we theoretically derive and propose a holistic framework that covers the major elements of social media, drawing on theories from marketing, psychology, and sociology. We continue to support and detail these elements — namely ‘motives,’ ‘content,’ ‘network structure,’ and ‘social roles & interactions’ — with recent research studies. Second, based on our theoretical framework, the literature review, and practical experience, we suggest nine guidelines that may prove valuable for designing appropriate social media metrics and constructing a sensible social media dashboard. Third, based on the framework and the guidelines we derive managerial implications and suggest an agenda for future research. © 2013 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.
Fast Labeling and Transcription with the Speechalyzer Toolkit
(2012)

We describe a software tool named “Speechalyzer” which is optimized to process large speech data sets with respect to transcription, labeling and annotation. It is implemented as a client server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation. The application is mainly the processing of training data for speech recognition and classification models and performing benchmarking tests on speech to text, text to speech and speech categorization software systems.
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables
(2017)

Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is selectively labeled, in the sense that the observed outcomes are themselves a consequence of the existing choices of the human decision-makers. For instance, in the context of judicial bail decisions, we observe the outcome of whether a defendant fails to return for their court appearance only if the human judge decides to release the defendant on bail. This selective labeling makes it harder to evaluate predictive models as the instances for which outcomes are observed do not represent a random sample of the population. Here we propose a novel framework for evaluating the performance of predictive models on selectively labeled data. We develop an approach called contraction which allows us to compare the performance of predictive models and human decision-makers without resorting to counterfactual inference. Our methodology harnesses the heterogeneity of human decision-makers and facilitates effective evaluation of predictive models even in the presence of unmeasured confounders (unobservables) which influence both human decisions and the resulting outcomes. Experimental results on real world datasets spanning diverse domains such as health care, insurance, and criminal justice demonstrate the utility of our evaluation metric in comparing human decisions and machine predictions.
Out-of-order transmission enabled congestion and scheduling control for multipath TCP
(2016)

With development of wireless communication technologies, mobile devices are commonly equipped with multiple network interfaces and ready to adopt emerging transport layer protocols such as multipath TCP (MPTCP). The protocol is specifically useful for Internet of Things streaming applications with critical latency and bandwidth demands. To achieve full potential of MPTCP, major challenges on congestion control, fairness, and path scheduling are identified and draw considerable research attention. In this paper, we propose a joint congestion control and scheduling algorithm allowing out-of-order transmission as an overall solution. It is achieved by adaptive window coupling, congestion discrimination, and delay-aware packet ordering. The algorithm is implemented in the Linux kernel for real-world experiments. Favorable results are obtained in both shared or distinct bottleneck scenarios.
Neuronal factors determining high intelligence.
(2016)

Many attempts have been made to correlate degrees of both animal and human intelligence with brain properties. With respect to mammals, a much-discussed trait concerns absolute and relative brain size, either uncorrected or corrected for body size. However, the correlation of both with degrees of intelligence yields large inconsistencies, because although they are regarded as the most intelligent mammals, monkeys and apes, including humans, have neither the absolutely nor the relatively largest brains. The best fit between brain traits and degrees of intelligence among mammals is reached by a combination of the number of cortical neurons, neuron packing density, interneuronal distance and axonal conduction velocity--factors that determine general information processing capacity (IPC), as reflected by general intelligence. The highest IPC is found in humans, followed by the great apes, Old World and New World monkeys. The IPC of cetaceans and elephants is much lower because of a thin cortex, low neuron packing density and low axonal conduction velocity. By contrast, corvid and psittacid birds have very small and densely packed pallial neurons and relatively many neurons, which, despite very small brain volumes, might explain their high intelligence. The evolution of a syntactical and grammatical language in humans most probably has served as an additional intelligence amplifier, which may have happened in songbirds and psittacids in a convergent manner.
Implications of modified waterfall model to the roles and education of health IT professionals
(2012)

Electronic Health Records (EHRs) are believed to have the potential to enhance efficiency and provide better health care. However, the benefits could be easily compromised if EHRs are not used appropriately. This paper applies a modified waterfall life cycle model to evaluate the roles of health IT professionals in the adoption and management of EHRs. We then present our development of a Masters' program in Medical Informatics for the education of health IT professionals. We conclude that health IT professionals serve key roles in addressing the problems and concerns and help fulfill envision of EHRs.
A SUMMARY REVIEW OF VIBRATION-BASED DAMAGE IDENTIFICATION METHODS
(1998)

This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based vs. non-model-based methods and linear vs. nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.
Context Based Approach for Second Language Acquisition
(2018)

SLAM 2018 focuses on predicting a student’s mistake while using the Duolingo application. In this paper, we describe the system we developed for this shared task. Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en). We conduct an ablation study with several features during the development of this system and discover that context based features play a major role in language acquisition modeling. Our model beats Duolingo’s baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812). Our work makes a case for providing favourable textual context for students while learning second language.
Library Anxiety: A Grounded Theory and Its Development
(2015)

This qualitative study explored the feelings of students about using the library for research. Personal writing, collected in beginning composition courses over a two-year period, was analyzed for recurrent themes. It was found that 75 to 85 percent of the students in these courses described their initial response to library research in terms of fear. Three concepts emerged from these descriptions: (1) students generally feel that their own library-use skills are inadequate while the skills of other students are adequate, (2) the inadequacy is shameful and should be hidden, and (3) the inadequacy would be revealed by asking questions. A grounded theory of library anxiety was constructed from these data.
VEGF-A stimulates lymphangiogenesis and hemangiogenesis in inflammatory neovascularization via macrophage recruitment.
(2004)

Lymphangiogenesis, an important initial step in tumor metastasis and transplant sensitization, is mediated by the action of VEGF-C and -D on VEGFR3. In contrast, VEGF-A binds VEGFR1 and VEGFR2 and is an essential hemangiogenic factor. We re-evaluated the potential role of VEGF-A in lymphangiogenesis using a novel model in which both lymphangiogenesis and hemangiogenesis are induced in the normally avascular cornea. Administration of VEGF Trap, a receptor-based fusion protein that binds and neutralizes VEGF-A but not VEGF-C or -D, completely inhibited both hemangiogenesis and the outgrowth of LYVE-1(+) lymphatic vessels following injury. Furthermore, both lymphangiogenesis and hemangiogenesis were significantly reduced in mice transgenic for VEGF-A(164/164) or VEGF-A(188/188) (each of which expresses only one of the three principle VEGF-A isoforms). Because VEGF-A is chemotactic for macrophages and we demonstrate here that macrophages in inflamed corneas release lymphangiogenic VEGF-C/VEGF-D, we evaluated the possibility that macrophage recruitment plays a role in VEGF-A-mediated lymphangiogenesis. Either systemic depletion of all bone marrow-derived cells (by irradiation) or local depletion of macrophages in the cornea (using clodronate liposomes) prior to injury significantly inhibited both hemangiogenesis and lymphangiogenesis. We conclude that VEGF-A recruitment of monocytes/macrophages plays a crucial role in inducing inflammatory neovascularization by supplying/amplifying signals essential for pathological hemangiogenesis and lymphangiogenesis.
Psychological Needs as a Predictor of Cyber bullying : a Preliminary Report on College Students
(2009)

Recent surveys show that cyber bullying is a pervasive problem in North America. Many news stories have reported cyber bullying incidents around the world. Reports on the prevalence of cyber bullying and victimization as a result of cyber bullying increase yearly. Although we know what cyber bullying is it is important that we learn more about the psychological eff ects of it. Th erefore, the aim of the current study is to investigate the relationship between psychological needs and cyber bullying. Participants of the study included 666 undergraduate students (231 males and 435 females) from 15 programs in the Faculty of Education at Selcuk University, Turkey. Questions about demographics, engagement in and exposure to cyber bullying, and the Adjective Check List were administered. 22.5% of the students reported engaging in cyber bullying at least one time, and 55.3% of the students reported being victims of cyber bullying at least once in their lifetime. Males reported more cyber bullying behavior than females. Results indicate that aggression and succorance positively predict cyber bullying wheras intraception negatively predict it. In addition, endurance and aff iliation negatively predict cyber victimization. Only the need for change was found as a positive, but weak predictor of cyber victimization. In light of these findings, aggression and intraception should be investigated further in future research on cyber bullying.
Real-time detection of students' emotional states in the classroom
(2017)

Today, as social media sharing increases, the instant emotional state of the students changes frequently. This situation is greatly affecting the learning process as well as the motivation of the students in the classroom. Sometimes, it is insufficient for the educator to observe the emotional states of students. Therefore, an automatic system is needed that can detect and analyze the emotional states of students in the classroom. In this study, an auxiliary information system, which uses image processing and human-computer interaction, has been developed that would be used in the field of education. In this system, the dataset obtained from the students' faces was tested using various machine learning algorithms. As a result, the accuracy of this system was found as 97.15% using support vector machine. This system is aimed to direct the educator to communicate with the students and to increase their motivation when necessary.
Automatic parking identification and vehicle guidance with road awareness
(2016)

Advanced driver assistance systems (ADAS) are becoming more common in safety and convenience applications. The computer vision based ADAS described in this paper, is an add-on system, suitable to variety of cars. It detects vacant legal parking spots, and safely guides the vehicle into the selected parking. Detection can be performed in both indoor parking lots and along roadsides. The system is composed of three standard computer-connected webcams, which are attached to the vehicle. Upon slowing down, the system starts searching automatically for a right hand-side vacant parking spot, while being aware to parking color signs. Once detected, the parking orientation is determined, and the driver is notified. Once a parking is selected by the driver, the relative position between the vehicle and the parking spot is monitored. Vocal and visual parking guidance instructions are presented to the driver. In addition, if during parking, an object is moving on the road towards the car, a safety alert is given. The system is universal in the sense that, as an add-on system, it can be installed on any private 4-wheeled vehicle, and is suited to urban driving environment.
Deep Learning for Sensorless 3D Freehand Ultrasound Imaging
(2017)

3D freehand ultrasound imaging is a very promising imaging modality but its acquisition is often neither portable nor practical because of the required external tracking hardware. Building a sensorless solution that is fully based on image analysis would thus have many potential applications. However, previously proposed approaches rely on physical models whose assumptions only hold on synthetic or phantom datasets, failing to translate to actual clinical acquisitions with sufficient accuracy. In this paper, we investigate the alternative approach of using statistical learning to circumvent this problem. To that end, we are leveraging the unique modeling capabilities of convolutional neural networks in order to build an end-to-end system where we directly predict the ultrasound probe motion from the images themselves. Based on thorough experiments using both phantom acquisitions and a set of 100 in-vivo long ultrasound sweeps for vein mapping, we show that our novel approach significantly outperforms the standard method and has direct clinical applicability, with an average drift error of merely 7% over the whole length of each ultrasound clip.
EPBC: Efficient Public Blockchain Client for lightweight users
(2017)

Public blockchains provide a decentralized method for storing transaction data and have many applications in different sectors. In order for a user to track transactions, a simple method is that every user keeps a local copy of the entire public ledger. Since the size of a ledger keeps growing, this method becomes increasingly less practical, especially for lightweight users such as IoT devices and smartphones. In order to deal with this problem, there have been some proposals. However, existing solutions either achieve a limited storage reduction (e.g., simple payment verification), or rely on some strong security assumption (e.g., the use of trusted server). We propose EPBC, a novel and efficient transaction verification scheme for public ledgers, which only requires lightweight users to store a small amount of data that is independent of the size of the blockchain. We analyze EPBC's performance and security, and discuss its integration with existing public ledger systems. Experimental results confirm that EPBC is practical for lightweight users.
A unifying computational framework for motor control and social interaction.
(2003)

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.
A Review of Wideband Wide-Angle Scanning 2-D Phased Array and Its Applications in Satellite Communication
(2018)

In this review, research progress on the wideband wide-angle scanning two-dimensional phased arrays is summarized. The importance of the wideband and the wide-angle scanning characteristics for satellite communication is discussed. Issues like grating lobe avoidance, active reflection coefficient suppression and gain fluctuation reduction are emphasized in this review. Besides, techniques to address these issues and methods to realize the wideband wide-angle scanning phased array are reviewed.